影响力指数

63.07/100

前 4%

全站排名 #2,565

发表论文10 篇

平均评分5.7

年均产出5.0 篇/年

Murat Kocaoglu

Assistant Professor@Johns Hopkins University·美国·OpenReview

研究方向

Information theory · Causal inference · Generative adversarial networks · GAN · Causal discovery · Experimental design · Online learning · Counterfactual

7.8

Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits

NeurIPS 2024Poster

三作

6.3

Counterfactual Fairness by Combining Factual and Counterfactual Predictions

NeurIPS 2024Poster

5.8

Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models

ICLR 2024Poster

5.5

Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand

NeurIPS 2024Poster

三作

5.3

Sample Efficient Bayesian Learning of Causal Graphs from Interventions

NeurIPS 2024Poster

三作

4.8

Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference

ICLR 2024Rejected

二作

4.3

Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand

合作者 (20)

Murat Kocaoglu

Differentiable Constraint-Based Causal Discovery

Characterization and Learning of Causal Graphs from Hard Interventions

Root Cause Analysis of Failure with Observational Causal Discovery

Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits

Counterfactual Fairness by Combining Factual and Counterfactual Predictions

Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models

Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand

Sample Efficient Bayesian Learning of Causal Graphs from Interventions

Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference

Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand